Indexing and presenting content using latent interests

ABSTRACT

Systems and methods are disclosed for a system to provide an interface that is dynamic and that provides selectable links in response to a query for products in an electronic marketplace, where the selectable links are titled with the query and portions of reviews for products associated with the query. The system is configured to select feedback for items purchased from an electronic marketplace. Descriptors from the feedback are generated. In response to a query for the one or more of the items in the electronic marketplace, a determination is made that portions of the descriptors provide detail responsive to the query. An interface is displayed including selectable links titled with the query in combination with the portions of the descriptors. In response to selection of one of the selectable links, a portion of the items are displayed.

BACKGROUND

Static databases and interfaces for searching provide static resultsafter matching a query to a related tag associated with entries storedwithin the database. The static nature of the databases and theinterfaces result from such matching which may be by a predeterminedmapping of queries to the related tags. When reviews are ultimatelyprovided for a result in a search service (e.g., an item purchased aftersearching using a query in an electronic marketplace), the reviews arefor the benefit of a subsequent user of the search service. In anattempt to find their intended result, users are left with having toscroll through multiple pages of an interface of the search service,having to read through multiple reviews in multiple pages, having torequest the additional pages from a server, and having to endure latencyissues at each interaction with the server and with the use of thestatic databases and interfaces associated with the search service. Forexample, users searching for shoes for a particular activity may findpages upon pages of results that provide all shoes available within thestatic database. This requires users to endure at least the abovetechnically disadvantaged process in their search to filter through themany results.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an example of a window of display content that can bepresented in accordance with various embodiments.

FIG. 2 illustrates an example system architecture for performing thedisclosed embodiments in accordance with an aspect of this disclosure.

FIG. 3 illustrates example system architecture for performing thedisclosed embodiments in accordance with another aspect of thisdisclosure.

FIG. 4 illustrates an example of a window of modified interface that maybe dynamic and that can be presented in accordance with variousembodiments.

FIG. 5 illustrates an example of semantic processing for machinelearning in accordance with various embodiments.

FIGS. 6A and 6B illustrate other examples of semantic processing formachine learning in accordance with various embodiments.

FIG. 6C illustrates another example of using knowledge graphs to findvariations of descriptors in reviews used in the machine learningprocess for query assists in accordance with various embodiments.

FIGS. 7A and 7B illustrate example process flows to index and presentsearch results using feedback, in accordance with various embodiments.

FIG. 8 illustrates example components of a computing device that can beutilized in accordance with various embodiments.

DETAILED DESCRIPTION

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Systems and methods in accordance with various embodiments of thepresent disclosure may overcome one or more of the aforementioned andother deficiencies experienced in conventional approaches to indexingand presentation of content, such as results for a current query in anelectronic marketplace of items. For example, using feedback left afterprior confirmed purchases of an item or from other trusted sources(e.g., a review left for a purchased item at a manufacturer's website),subsequent users may be presented with a query assist suggesting queryoptions and/or categories for items possibly responsive to a subsequentquery.

In an example, systems and methods herein parse feedback, such asreviews, for a first item that may be part of confirmed purchases at theelectronic marketplace or left at a manufacturer's website. Descriptorsfrom the feedback are determined and are indexed in an index database.The descriptors are portions of the feedback that describe at least onepossible latent interest of an item from the feedback. The descriptorsmay describe activities, audiences, interests, and other pre-determinedlatent interests that are described in the feedback after users haveused the item. Such latent interest may not be found in manufacturerdescriptions of items and allows for further customer-friendlycategorizing of items for a subsequent user of the system. Thedescriptors may be used to train a neural network with respect to anassociated query. The query has association to an item or thedescriptors may be indexed with association to the purchased item, forinstance. Then, a subsequent user may be provided with one or moreportions of the descriptors as part of a subsequent query—as queryassists or categories, for instance—in an interface during a query timefor the subsequent query. This may be by passing the query through thetrained neural network and finding semantically classified descriptors.As the subsequent query is entered into a search area, a menu or otherdisplay region provides the subsequent query in combination with atleast one portion of the semantically classified descriptors asselectable links to filter an initial set of items that is generallyassociated with the query itself. The resulting items are a categorizedversion of the initial set of items that are categorized by thesemantically classified descriptors based on the association therebetween.

For example, prior feedback or prior reviews for shoes previouslypurchased may include statements such as “these shoes are great forhiking” or “I ran a marathon with this shoe and it felt really great.” Afilter module including a parser and a learning module may be trainedwith descriptors from the feedback or reviews associated with the shoespreviously purchased. The filter module may be trained using itslearning algorithms, including a semantic similarity algorithm, todetermine that hiking and running are the descriptors from the feedbackor reviews for the previously purchased shoe, for instance. Thedescriptors are determined because of their latent relationship orinterest to features of the shoe—e.g., to activities or other featuresthat share semantic similarity with the shoe—such as the hiking or therunning activities, from the above example feedback or review. Thesedescriptors—hiking or running—and their relationship to the query arestored by training a neural network to recognize the descriptors assemantically associated with the query, for instance. A furtherassociation is stored between the query and the shoe. Alternatively,these descriptors are separated and stored with information about theirrelationship to the item and the query. In a further verificationprocess, only reviews for confirmed purchases and only reviews withpertinence may be parsed and have its descriptors determined. Pertinentreviews may be determined by running the review words through a neuralnetwork trained with semantic vocabulary from a query term and which maybe separate from the above referenced neural network.

The descriptors are then used to dynamically modify the interface or aportion of the interface, such that when a subsequent user is searchingfor “shoes,” a drop-down menu or other interface aspect displaysselectable links forming query assists that assist the subsequent userfor finding the right (or possibly intended or desired) item in theelectronic marketplace—e.g., “shoes . . . for hiking,” “shoes . . . forrunning,” etc. These additional options were previously unavailable asmanufacturers may not advertise these items for hiking or running, forinstance, while prior users used them for these purposes and provided afeedback indicating suitable use outside the advertised use. Theselectable links are, therefore, titled with the query in combinationwith at least one portion from the descriptors. While this is anon-limiting example of improved indexing, searching, and contentpresentation, this may also be extended to provide additionalinformation or categorizing in the content presentation—such asincluding more exact information (e.g., excerpts) of the reviews or thefeedback in the interface. When the selectable link is clicked, itemsassociated with the descriptors and the query—that is items that werestored in a content database with an association to the descriptors andthe query—are provided in a ranked order with the most relevant item atthe top of a listing of entries.

Various other functions can be implemented within the variousembodiments as well as discussed and suggested elsewhere herein.

In the present implementation, a technical benefit is achieved byensuring that a portion of filtering of the content is now performedremotely at the server using a specific filter that cycles feedback orreviews from resources not easily available to a subsequent user toimprove content presentation, indexing, and speed of content delivery tothe subsequent user. This ensures that the content is providedresponsive to a search in a partly filtered manner, which does notrequire excessive client-side filtering. Alternatively, this ensuresthat all filtering is performed at the server and no filtering optionsare offered on the client-side. In addition, by moving the filter to theserver and making it remote from the client, the technical benefitsgained include an ability to dynamically modify the categories ofresulting items to match presently described usage. Another technicalbenefit gained is the ability to securely manage the filtering of dataon the server side that overcomes prior reliance on user's input whichmay incorrectly filter away the most relevant items. These benefits areadditional to resolving at least the latency and poor contentpresentation issues raised with static databases and with staticinterfaces under existing search technologies. The present solution,additionally, represents dynamic interfaces for the improved contentpresentation and a new type of analysis that relies on feedback andreviews to improve the indexing and speed of content delivery that wasnot previously considered.

In an example, modules performing at least the parsing and thedetermination of descriptors may constitute a filter that relies onsemantic learning. The descriptors of reviews may include latentinterests that may be missing from a brochure or manufacturer-providedinformation for the products or items that describe its intended usage.Subsequently, when a query is received for the products or items in anelectronic marketplace, it may be determined that portions of thedescriptors provide details responsive to the query. When suchdetermination is made, along with a further determination that the querycorresponds to the products or items available in a database, then theinterface that may be dynamic is provided to the querying device. In aninstance, the interface includes listing of entries that are responsiveto the query as selectable links. This disclosure uses listing ofentries or listing of items interchangeable to refer to results that mayinclude item by names or descriptions, if applied in an electronicmarketplace. For example, the items are not the actual items, which maybe referenced by the names or the description provided in soft format.In an alternate implementation of the use of listing of entries, thelisting of entries maybe injected into the interface at display time orafter the display time of the interface. Each selectable link is titledwith the query in combination with at least one portion from thedescriptors.

FIG. 1 illustrates an example of an interface 102 that can be presentedin an application 100 on a screen of a computer in accordance withvarious embodiments. In an example, the interface 102 is a window, awebsite, or application screen of a web browser or a stand-aloneapplication for interacting with the display content and for executingdynamic scripts within the interface. A user may search or interact withthe interface via a search field 108. The content may be news articles,products, items, services, or other electronic or non-electronic media.A query 118 is provided in the search field 108 in one implementation ofthe searching or the interaction with the interface. When the submitoption is selected, the search may be initiated and processed on thecomputer or on a server as discussed subsequently in this disclosure. Inexample 100 of FIG. 1A, a query 118 for “Shoes” is entered into thesearch field 108, and the interface provides a results section 104—e.g.,a webpage or portion of a webpage 102. As illustrated, there are threeresults 110-114 in the interface, with an indication (e.g., down arrowwith text “MORE” at the bottom of the page 102 or the grey scrollindicator on the right of the page 102) for more results if theinterface is scrolled. The display content may each include summaryspecifications as shown in example 114 by reference numeral 116.Categories within the interface are presented on one side, region, orarea 106B of the results, while sponsored and other content may bedisplayed in other available sides, regions, or areas 106A.

Results 110-114 may be fetched based on prior behavioral clues fromusers' prior searches. For example, in line with a static database,items and item names that were previously searched by users and thatinformation may be used to skew the results. The results also shareterms with the query, which may form a basis for the searching andpresentation of the results 110-114 in an allocated area or slot, e.g.,results 104. Indeed, the provided title of the content of each searchresult 110-114 may reflect a pre-determined title, such as the item orproduct brand name or model number or number as it is stored orallocated for storage in the static database. The title may be static.For example, Hiker may be a brand or model name for the boots in entry110. The results 104 are, therefore, a skewed match from at least oneterm recited within an entirety of description available in a databasefor each item available through the interface 102. The description maybe stored in a static dataset to at least match with one or more queryterms.

Results 110-114, however, are limited to pre-defined descriptions frombrochures and other information provided for items in the staticdatabase. This may not reflect information from actual users, for actualuses, or from other sources for the items. Here, even though a reviewerof the results may then select an entry from the results 110-114 andread available reviews, the reviews may be to unrelated characteristicsassociated with the item that is subject of the user's query. Forexample, the review may describe shipping efficiency of a seller of theitem or may describe condition of the item as received. In addition,there may be several pages or entries of the reviews. The reviews mayalso be more information that does not address concerns of a user—e.g.,whether the item is good for a specific activity (e.g., hiking, running,events, etc.), a specific audience (e.g., parents, music lovers,travelers, etc.), a specific interest (e.g., charity, festivals, urbanexploration, etc.).

Moreover, as the database is static and provides no room for relatingspecific latent interests, to the query and to stored item descriptions,the results obtained from the database may be stagnant or dated. Newuses for existing items may be discovered by users and this informationmay not find its way to the dated or stagnant information of the staticdatabase. Therefore, relying on past user behavior alone may notgenerate information as to stored item descriptions. The staticquery-results interface is also limited to requiring a user to filterthe reviews to determine if items are relevant to the latent interestsof the user. As such, the process using a static database andquery-results interface is technically limiting.

One of ordinary skill would recognize that a website promoting orselling items, including services, and its hosting servers may notprovide support to dynamic interfaces or database structures capable ofupdating using review or feedback information from other websites or thereview portion of the website. Alternatively, it may be the case thatthe items provided are not usable for other latent interests andnegative tones of the feedback or review is not taken into account toaffect visibility in the rankings of results of certain items. Theresults may therefore be distributed over many pages requiring constantcommunication and receipt of information from a server as the resultsare scrolled. These issues may be a consequence of an inability of thehost servers to index data in a manner to include feedback or reviews,which require some of dynamic indexing to be able to account for thelatest reviews and feedback. In an example, indexing may be by storingdata as inter-referenced rows and/or columns in a multidimensionaltable. A trained neural network may be in the form of a trained datasetindexed in the manner described throughout this disclosure. These issuesmay also be a consequence of the host server's inability to efficientlyprocess data, resulting in latency issues as results are gathered fordisplay. This inefficiency also causes increased traffic as the userscrolls through items or reviews requiring the client device toconstantly involve the server for further listings. This results indecreased user experience. The solutions in this disclosure includessystems, and software or firmware configurations that are available tomodify the interface to include results that are in a dynamic form, suchas providing an option at the time of entry of the query to include thequery with category and/or additional information extracted fromfeedback or reviews left by users or prior purchasers of associateditems or products to the query.

The category and/or additional information represent latent interestfrom the feedback or review, which may correspond to an unwritteninterest in the query predicted by the system or method as a user typesin the query or portions thereof. In a deviation from the categoriesthat are pre-determined and provided in side, region, or area 106B, thecategories presented in the dynamic interface may be drawn from thelatent interest—e.g., when an item associated with the result isidentified from prior reviews as being particularly used for specificactivity, a specific audience, a specific interest, or other latentinterests, this information may be used as a category and/or queryassist in area 106B or in a separate menu provided at query time orsubsequent to a received query. A user may then select from the categoryor query assist (e.g., “shoes . . . for running, . . . for hiking,” “ .. . for formals,” “ . . . for children,” etc.) as opposed to thecategories presently shown in area 106B, which are based off of acatalog or a pre-determined standard category associated for suchproducts. A typical query for shoes may otherwise generally bring upshoes available in a database and may generally bring up standardcategories, such as men's shoes, women's shoes, etc., as categorized bya manufacturer. Furthermore, the general categories are not dynamic andmay not be within the latent interest categories. As a result, a websiteincorporating the present solutions, include configurations in anassociated host server, to reduced latency and reduce misdirectedtraffic using categorized results responsive to a query based at leastin part on prior feedback received.

FIG. 2 illustrates example system architecture 200 for performing thedisclosed embodiments in accordance with an aspect of this disclosure.Example environment 200 may include computing components and networkrelationships that are applicable for providing the content in thewebpage or interface 102 of FIG. 1. The example environment 200 includesa content server 210 for serving content, including all or a portion ofthe interface 102, in response to a search or interaction on the webpageor interface 102; a database 212 for storing content from contentproviders 204 (e.g., product information, service information,advertisement, and other related information; news, social media, andother product/service related content from which information is gleanedfor use in the present system. Further, advertisement networks mayprovide paid content (e.g., content in section 106A of content page102), and users with computing devices 202 may send queries or interactwith the content server to access the paid content (e.g., advertisement)or unpaid content (e.g., digital information for products and servicesthat may be referred to or available for purchase via the content server210).

Content providers 204 are able to utilize respective computing systemsand/or devices to interact with the content server 210 through thenetwork 208, for example, a local area network (LAN) or wide areanetwork (WAN), e.g., the Internet. Similarly, users with computingdevices 202 are able to utilize their respective computing device toaccess content (e.g., websites or stand-alone web enabled applications102) that may be offered through the content server 210 or via thecontent providers 204 over the network 208. For example, contentproviders 204 can provide content (e.g., webpages, product information,etc.) that is accessible over the network 208 (e.g., the Internet). Insuch an example, the content providers use the content server as a hostfor interacting with the users/client devices 202. In an alternateimplementation, the content server hosts its own services for providingcontent of the content providers 204, such as an electronic marketplace.In yet another alternative implementation, the content providers 204 mayutilize one or more of its own computing systems to provide a website orweb-enabled application that is accessible through the network 208. Insuch an implementation, the content server 210 may provide referrallinks for content to the content providers' websites for purchase ofassociated products and/or services.

The content providers' website or web-enabled applications may offeropportunities to present additional, and in some instances, paid contentto users accessing the website. For example, electronic advertisementsor other digital media may be provided for newly released products basedon the analysis of queries and news websites described herein. Thecomputing devices and/or systems for each of the content server 210,content providers 204, content webpages 206 (e.g., news and socialmedia), and users with computing devices 202 will each generally includememory for storing instructions and data, and at least one processor forexecuting the stored instructions that configure the computing devicesand/or systems to perform the features disclosed. Further, reviews orfeedback may be provided in content of a content provider 204 and may beretained by the content provider 204 or may be shared to the contentserver 210. In either case, the content provider 204 may provideexplicit access to the reviews and feedback for crawling and indexing bythe content server 210.

When a user with a computing device 202 uses the computing device toaccess content from the content server 210 or content providers 204, therelevant content provider 204 can send, either directly or via thecontent server 210, responsive content to the computing device 202. Thisaccess for content can include various requests or searches to findspecific content hosted by the content server 210 or content providers204. Further, content server 210 may be a special status host and mayhave special access ability to stored content and indexed feedback fromvarious content providers 204 that are hosted on or released to contentwebpages 206. This process allows at least a portion of content andfeedback to be dynamically updated, and allows the feedback to beindexed to provide sufficient new information to interested parties infuture searches via content server 210. Accordingly, content server 210may include cookies, authentication certificates, or signed certificatesto enable such access to protected content hosted in content webpages206.

FIG. 3 illustrates further example system architecture 300 forperforming the disclosed embodiments in accordance with a further aspectof this disclosure. The system architecture 300 includes contentproviders 306 in communication with content servers 316 and with clientdevices 302, via network 314. As noted with respect to FIG. 2, thecontent providers 306 may incorporate features of the content servers316 as discussed subsequently herein to process content in a similarmanner as the content servers 316. Client devices 302 and contentprovider devices 306 can include any processor and memory basedelectronic devices with capabilities as disclosed herein, but at leastwith the capability to execute computer-readable instructions in abrowser or to execute computer-readable instructions with or without abrowser. These electronic devices are described in detail below andinclude specific configuration to perform the functions herein. Suchelectronic devices may include personal computers, tablets, ultrabooks,smartphones, cell phones, wearable watches and related devices, handheldmessaging devices, laptop computers, set-top boxes, personal dataassistants, electronic book readers and the like. Each of theseelectronics devices may be configured to include a browser or astand-alone application 304/308 that is capable of being configured inthe manner of this disclosure. In an implementation, content providers306 may use one or more of content servers 316 to run tests on hardwareand software features or services offered in the system architecture300. Users or consumers of electronic or online products and/or servicesuse the client devices 302 to interface with a website providing suchcontent, including reviews and feedback to either the content providersdirectly or to the content server 316.

Network 314 can include any appropriate network, including an intranet,the internet, a cellular network, a local area network or any other suchnetwork or combination thereof. The network could be a “push” network, a“pull” network, or a combination thereof. In a “push” network, one ormore of the servers push out data to the client device. In a “pull”network, one or more of the servers send data to the client device uponrequest for the data by the client device. Components used for suchsystem architecture 300 can depend at least in part upon the type ofnetwork and/or environment selected. Protocols and components forcommunicating via such a network are well known and will not bediscussed herein in detail. Communication over the network 314 can beenabled via wired or wireless connections and combinations thereof. Inthis example, content server 316 may include one or more local serversin communication with each other and with other remote servers via thenetwork 314. In an example, the content server 316 includes a web serverfor receiving requests and serving content from the client devices 302and/or the content providers 306. In response thereto, although forother networks, an alternative device serving a similar purpose as anyone of the content server 316 could be used, as would be apparent to oneof ordinary skill in the art upon reading this disclosure.

In a further example, the content providers 306 may include a contentconsole 308 for communicating with the content server 316. The contentconsole 308 may be an internet-enabled application (e.g.,browser/stand-alone application) that is configured to execute on thecontent provider 306 and that is configured to communicate with thecontent server 316 in the manner described herein. The communicationsbetween the content server 316 and the servers/devices of FIG. 3 are viaan interface or networking component 318, such as a network interfacecard or a wireless interface. In alternate embodiments, the systemarchitecture 300 is maintained internal and confidential between thecontent providers 306 and the content server 316 during theconfiguration stages. Accordingly, one or more components or modules inthe system architecture 300 are isolated from external influence by anyknown security methods, including firewalls, during configurationstages. Alternatively, sections of the one or more components or modulesin the system architecture 300 are available within one or more contentproviders 306. In yet another alternative implementation, sections ofthe one or more components or modules in the system architecture 300 maybe secure, while other sections of the one or more components or modulesmay be available in the public domain to interface with client devices302. In accordance with such alternate implementations, one or morecomponents or modules in system architecture 300 may also be virtualmachines or operate in a virtual environment for performing one or moreof the features disclosed herein.

Further, in the example architecture 300 of FIG. 3, real-time or dynamicresults are provided for queries from the client devices 302. Forexample, queries are addressed in real-time or dynamically via servermodules 324, which may include machine learning capabilities, interfacegenerator 338, and query assist 336. Modules 324 and 336 maycooperatively function under two or more modes. In one of the modes, themachine learning features of select server modules 324 (e.g.,crawler/review analyzer 332 and review indexer 330) may constantlyimprove upon itself using each new review or feedback from confirmedpurchases of certain items to improve their respective machine learningalgorithms to ensure robust functionality. Machine learning aspects areprovided in the examples of FIGS. 5 and 6A-B. Such a mode may bereferred to as a training mode. A mode in which the trained neuralnetwork is actively used for finding descriptors from reviews may thenbe an active mode for the neural network.

In an application of neural networks for the machine learningoperations, the machine learning aspects of the modules may train one ormore neural networks with each of the new review or feedback for apurchased item and for the used query, while running active operationswith a copy of a previously-trained neural network. Once the new reviewor feedback has been trained to one or more inactive neural networks,the one or more inactive neural networks may be activated and thepreviously-trained neural networks may be inactivated for training. Theuse of the neural network to provide newly formed categories and/or tomodify the interface by providing query assist represents an active useof a trained neural network. Alternatively, bulk new review or feedbackfor confirmed purchases is periodically received and parsed, and is alsoavailable to train one or more neural networks during an assigneddowntime for each associated item. The trained neural network may beassigned a word from the item to which multiple query assist terms aretrained.

In content server 316, the queries from client devices 302 may bereceived via module 326. Results webpage(s) or an interface is generatedby module 338. In an example, the interface generator 338 is a modulethat includes HTML® and various dynamic scripts that are formatted forrendering on the client device 302. The dynamic scripts enableadditional requests for information to populate an HTML® formatted pageupon rendering on the client device, for instance. In addition, contentserver 316 includes a query monitor 328 for determining if there are newqueries in the received queries.

In an example, new queries or new reviews may be used to search forrelevance (e.g., semantic similarity) with each other and to items (bythe titles or descriptions of the items, for instance) in the contentdatabase 320 after a confirmed purchase. When the relevance isdetermined, the portions of the reviews (i.e., descriptors) providingsemantic similarity for the queries is used to retrain or further traina trained neural network in the index database 322. The new queries ornew reviews are also associated with the item purchased after the queryis processed to generate an item from the content database 320. In thismanner, new or subsequent queries may be incorporated in the system byrelating them with descriptors for providing query assists at theearliest possible time even though the query was never previously used.This may dynamically occur, where the system determines that a firsttime query is possibly related to descriptors and to an item for whichcategories were previously determined using prior reviews or feedback.

Query reviewer 330 is a module that may be separate or part of the querymonitor 328, and that may review the queries via the above-referencedsemantic matching process to determine if any of the new queries havesemantic associations with new or prior reviews or feedback. Thisensures that the query assist is current with new terminology in newreviews and with new terminology in new queries. This also ensures thatno matter how new a query may be, a query assist may be provided so thata user is at least somewhat likely to find what was initially intended.In the event that a semantic match is not found—evidenced, for example,by a lack of classification of the new queries in a trained neuralnetwork with existing descriptors—then the trained neural network may beretrained with the new queries.

In a further aspect, the descriptors are determined from reviews orfeedback parsed from a webpage after a confirmed purchase or left onother trusted websites as described earlier. The descriptors may bedetermined by a machine learning algorithm of module 334, as havingsimilarity and/or semantic relationships to terms in a query after theconfirmed purchase. Alternatively, the similarity and/or semanticrelationships used to determine the descriptors are based in part on adescription of an item on a website related to the item. In this manner,even though the description may be limiting and not be used directly,semantic similarity is confirmed to a feedback left for the item becauseof latent relationships in the language used in the feedback to the itemdescription. The similarity and/or semantic relationships may alsopertain to implementations where the terms in the query and the terms inthe feedback share a relationship, including common occurrences—togetherin a sentence of the review—for instance. A proximity feature of thesemantic relationships may also take into account that the commonoccurrences of the terms in the review or feedback are within apredetermined number of words from each other. Furthermore, descriptorsmay be used alone or with excerpts, as an aspect of this disclosure, toprovide further content for the title of the selectable links. Thisallows the query assists to provide more than a latent interest as acategory, but also to provide a personalized message with the latentinterest, for instance. The title to the selectable links may then be aportion of the descriptors or all of the descriptors generated from areview or feedback. The excerpts may be stored in the index databasewith the descriptors for retrieval.

Reliance on semantic relationships between the query terms and thereview terms in the machine learning process then inform the system thata subsequent query for an item, e.g., “gaming consoles,” “xbox one,”etc., has terms with semantic association to review terms extracted andindexed from prior reviews as the descriptors. For example, the systemmay parse the following prior reviews: “the xbox one is a great gamingconsole for professional gamers” and “the xbox console is excellent formobile game”; then, assuming that these prior reviews are from confirmedpurchases or from other confirmed or trusted sources (e.g., reviews onthe manufacture's website), the descriptors are obtained. Thedescriptors may be obtained by a neural network trained to rejectarticles in the language or that can reject superfluous terms, whileretaining nouns and adjectives, or by retaining phrases for use asexcerpts. These nouns, adjectives, or retained phrases may be semanticversions of the query. Separately, the descriptors are identified andtrained to a neural network as described with respect to FIGS. 5, 6A,and 6B. The neural network used for training between reviews to queriesis referred to herein as a review-to-query neural network to distinguishfrom other possible machine learning processes used in a preliminarystep to select reviews for training the review-to-query neural network.

The determination of the descriptors then provides the index database322 with terms to be indexed for subsequent retrieval. Alternatively thetrained review-to-query neural network may be stored in the indexdatabase 322. In the above example reviews, a review-to-query neuralnetwork may be trained with descriptors (using words or combinationsthereof) from the reviews: “professional garners,” “professional,”“garners,” “mobile games,” mobile” and/or “games.” This may be byrejecting other language in the review and/or by determining that theseterms are semantically related to the query terms of “gaming consoles”and “xbox” from prior searches leading to the confirmed purchases orbeing from the confirmed or trusted sources. The descriptors may bestored as they are picked from the review—in pointers or matrices—withassociation to the items purchased after the query was completed and/orto the query itself. The review-to-query neural network providesassociation made between the query and the descriptors so that when asubsequent query is input to the review-to-query neural network,descriptors with semantic matching are provided as output. The outputdescriptors are then combined with the subsequent query and provided asquery assist titles with an underlying selectable link. A selection tothe selectable link causes items associated with the query to be furtherfiltered according to the items' association with the descriptor. Forexample, the trained review-to-query neural network, now understandingthe association of past queries to the descriptors is able to providequery assists so that when a subsequent user enters the subsequent querysuch as “gaming consoles” in a search area of an interface, theinterface is modified to present selectable links titled with the queryand a portion or all of the descriptors. The subsequent user queryingfor “gaming consoles,” therefore, is presented with the following queryassists: “gaming consoles . . . for mobile games” and “gaming consoles .. . for professional garners.” Then selection of the “gaming consoles .. . for mobile games” will cause an underlying selectable link to filtergaming consoles to the ones identified in prior reviews as for mobilegames.

In a further aspect, the portion of the descriptors may be associated tothe items, in the content database 320, as a category for the items soas to enable the above filtering. For example, an item entry for thegaming consoles in the content database includes a reference to eachdescriptor. This reference may be by a tag entry for the items in thecontent database and the same tag entry may be used for the associateddescriptors in the index database. Such an association ensures that whenthe interface generator generates the interface with the contentinformation for items responsive to the search, then the index databaseprovides the categories or other information (e.g., excerpts of reviews)for the query assist module 336. The content information and the queryassist are injected into the interface from the interface generator 338.Subsequently, the query assist may modify the interface in real time byproviding further query assists if the user performs a second searchand/or makes changes to the initial search. As such, the contentdatabase may also provide responsive items as the search changes. In animplementation, the content and the index databases 320, 322 are aunified database with linked keys to provide reference there between.

In an example, there may be a process to qualify received reviews priorto usage of the qualified reviews to train the review-to-query neuralnetwork. Such an implementation improves efficiency of computing devicesby requiring the review-to-query neural network to only be trained whenrelevant reviews are available. The processing of excessive inputs in aneural network—such as for training purposes—is an extremelyprocessor-intensive operation. Any reduction in the processor usage aswell as reduction in time for training of neural networks will allow forthe active usage (e.g., uptime) of the trained neural network. In anexample, a qualifier neural network may be used for qualifying that areview is indeed relevant prior to its usage in training thereview-to-query neural network. For example, reviews discuss otherunrelated issues to an item than the actual item. Such issues may beshipping issues, condition of the item, etc. In an implementation, onlyactual use, target audience, or interest are latent interest bases forsemantic similarity with query terms. There may be other latentinterests that may be addressed according to an intended usage of thepresent disclosure. As such, a qualifier neural network may be able toweed out the unrelated reviews from the received reviews by aclassification process where words in the reviews are assigned valuesand are subject to discrimination.

The classification process will identify words such as “shipping” and“quality” as outside a desired classification area in a two-dimensional(2D) classification process adopted in the qualifier neural network, forinstance. One such 2D classification process may allocate neighboringvalues to synonyms than antonyms for certain words. A classificationbased on such allocation then separates reviews with unrelated words(and their variations) from the reviews intended for the query. It willbe understood by a person of ordinary skill reading the presentdisclosure that “quality” may be an intended latent interest. When thisis the case, the review-to-query neural network may be trained to accept“quality” as a category and latent interest for query assist. Thequalifier neural network is also then trained to make a similaracceptance of reviews to one or more items that discuss quality.

The example process in FIGS. 5, 6A, and 6B are to such assignment ofvalues to words and/or phrases and to the use of these values to train aneural network and to verify the classification attained in the trainedneural network. This training, in turn, represents training of a neuralnetwork to classify select words—such as descriptors for queries in thereview-to-query neural network and relevant reviews over irrelevant onesin the qualifier neural networks. As such, the example process in FIGS.5, 6A, and 6B apply generally to both the qualifier neural network andthe review-to-query neural network. For the qualifier neural networks,tangential language not commonly used to describe the item itself—e.g.,discussion in a review as to installation required for an item may be anoutlier within the classification for a relevant review. However, it mayalso be the case that “installation” or “easy installation” described inreviews would be category or latent interest provided in response to aquery for an item that requires installation. As such, reviewsdiscussing ease of installation may be incorporated into thereview-to-query neural network and may be tagged to an item and to aquery, such that when the query is applied in a subsequent search, theinterface is modified to present the query in combination with theoption to find items with “easy installation”; e.g., for a query of“furniture,” a modified listing of results includes: “furniture . . .for easy installation” as a selectable option or link. In an example,the subsequent query may be determined as associated with a categorybased on a classification of at least one portion of the subsequentquery in a classified dataset that provides categories and content.Category-specific content may be determined for the category andportions of the descriptors may be then determined from thecategory-specific content.

In an example, variations to terms are considered within theclassification capabilities of the neural networks described herein. Anactivity described in a review is much more likely to be described inanother review bringing two assigned values to terms within the reviewstogether, even if they have slight variations—e.g., noun usage, verbusage, etc. This is possible when a review describes a shoe as “greatfor hikes” versus another review that describes a shoe as “excellentshoe for hiking the Appalachian Trail and fast shipping.” These tworeviews provide variations to the term “hike,” which is still relatedand still describing an activity that is strongly associated (by atleast semantic association/similarity) with a particular item and thatis not described in any manufacturer produced material for theparticular item. The qualifier neural network herein is able to qualifythese two reviews as proper for further classification with thereview-to-query neural network. However, the language of “fast shipping”or “no instructions provided,” etc., would classify outside of aclassification area that attempts to classify terms from the two reviewsunder a qualification of the two reviews.

Further, as both hiking-related reviews were left after a purchase of ashoe, then the query terms associated with that purchase alone, or withthe addition of a confirmation of the purchase of a shoe may be used todetermine feature descriptors (e.g., the activity of “hiking”) asrelevant to shoe. In addition, two or more words of parsed reviews orfeedback may be used in different combinations for the qualifier neuralnetwork as well to train the subsequent review-to-query neural networkfor semantic relationships of the review with the query terms.

In a further aspect, noun identifiers may be weighed preferably overarticles and other grammar portions, which may then be wholly or partlyrejected, from the reviews. Further, words of the reviews or feedbackare analyzed against individual words across multiple queries for aconfirmed purchase. In a process to eliminate false positives, the wordsmust be identified as similar and/or semantically similar by thequalifying neural network using prior queries or content stored in thecontent database 320 to ensure that queries or items exist to classifythe reviews or feedback with the words in content database 320. Indeed,if no content is seen as matching, then it is likely that the words orterms from the review relates to description that is improper or thatrelates to an item not previously indexed. Each word in a review may betaken with another word of the review to find correlation to the wordpair. In an alternate aspect, from one review, two words are taken as asingle word and then combined with one or more words to find correlationbetween the newly formed words and other groupings of review words orterms. Semantic relationships and/or similarities are measures fromassigned values to the terms in queries or the reviews that may bestored with the terms or that may be determined at query time and arethen applicable to provide a related term to each query entered into asearch area of the interface.

In an implementation, the descriptors, having been determined assemantically similar and/or plainly similar between received queries andreviews are then used to categorize items related to the queries. Forexample, instead of providing pre-determined categories as previouslynoted in regards to FIG. 1, the query assist 336 modifies, either atquery time or subsequent to query time, the interface by input to theinterface generator 338 to provide dynamic categories as noted inregards to FIG. 4. At query time, the query assist 336 provides thedynamic updates to the interface as the query is typed—either by letter,word, or phrase of the entered query. Further, when terms in queries areassociated with descriptors of index database 322 and to items within acontent database 320, then further relationships may be studied from theassociated descriptors and further categories may be provided for theitem. There may be a limit to this process as the existence of too manycategories with few items or overlapping items may confuse a reviewer.In an example, only certain regions of the interface are dynamic and theentire interface does not need refreshing or reloading for the presentlyrecited modification to be performed to the interface.

The crawler/review analyzer 332 uses web identifiers, such ashyperlinks, Uniform Resource Identifiers (URIs), or Uniform ResourceLocators (URLs) of review websites, product websites, or of internalwebpages of an electronic marketplace to determine new reviews fromtrusted sources. These may be confirmed reviews left at the contentprovider 306 or the content or third-party content 310, instead of on anelectronic marketplace hosted by content server 316. As newly retrievedreviews are obtained, it is likely to be the case that the webpagesproviding these reviews (e.g., from content provider 306 or the contentor third-party content 310) include a link to the product description ordomain of the manufacturer, retailer, or distributor of the newlyreleased item. Alternatively, when stored in the electronic marketplaceof the content server 316, such reviews may be stored with the itemsinformation in the content database 320, which is also shown asavailable to the crawler/review analyzer 332 and the review indexermodule 334. The hyperlinks determined as embedded in such webpages andin other trusted sources providing the reviews—e.g., certain socialmedia webpages 310, may be extracted and provided as part of the reviewsfor qualification and for description of other latent interests forsubsequent queries.

The web identifiers determined as embedded in manufacturer websites andtrusted webpages corresponding to third-party content 310 may beretrieved, analyzed, and indexed (with the descriptors, for instance) inthe index database 322 by the review indexer module 334. In an example,the at least one of the web identifiers of a content websitecorresponding to the third-party content may be parsed to determinedomain information. Then a confidence score may be determined asassociated with an entity owning or operating the domain based at leastin part on the domain providing information for the first item. Forexample, if the domain belongs to a distributor or manufacturer of anitem sold in the electronic marketplace, then reviews left in a websiteof the domain bears confidence for indexing in the index database 322.Particularly, when the confidence score exceeds a threshold value, thenthe reviews from the content website for the interest descriptors may beprovided for indexing. In a further aspect, parsing the content websitesis to select sets of contiguous words associated with the first item.When at least one set of contiguous words is selected, a count may beincremented to reflect this. Subsequently, the confidence score may bealso weighed favorably for the domain when the count satisfies athreshold.

The review indexer module 334 may retrieve and generate the associationbetween descriptors and query for the content database 320 and for theinterface generator 338. The query assist 336 may then retrieve contentand one or more portions of the descriptors (including excerpts ifavailable and if required) and provide this to modify the interfacegenerated by the interface generator 338. Further, the query assist 336may, alternatively or concurrently, modify the interface to provide newdescriptors over the at least one portion of the already provideddescriptors in the interface. In addition, the query assist 336 may bepart of the review indexer module 334, and may perform the modificationof the interface along with (e.g., concurrently with) the generation ofthe interface.

In an example, rankings are provided to the results based at least inpart on an amount of semantic similarity between the query and thedescriptors determined as sharing semantics with the query. Theinterface may then include ranked query assists of the combinations ofthe query and descriptors and/or ranked results—e.g., 1, 2, 3 . . .etc.—representing a maximum number of query assists and/or of resultsfor one page of the display content. The maximum number of query assistsand/or of results is configurable by the user to display more rankedresults per page of the interface. When the modification to theinterface is performed, content for the query, including iteminformation with the query assist may be provided above the rankeditems. Such item information may include an identifier and a portion ofan underlying specification or other information for the item.Alternatively, the item information replaces a prior result list and newentries are provided with new categories marked in an area adjacent tothe item information. In one aspect, content for the new item or newcategory information is provided in a “1” ranking, while the existinglisting of items is moved down to 2 or subsequent ranks. Such amodification may occur dynamically, as the query is entered, changed,and/or as required for generating results more in tune with the query,based in part on the descriptors from prior reviews determined asassociated with the query. Alternatively, the interface includes an areafor the new content under a special ranking—e.g, a rankingrepresentation of “0” implying a default status at the top of one ormore pages in the display content. As such, the intervening ranking maycorrespond to modification of the existing rank or insertion of aranking or placement that would position content with certain items formore visibility than the remaining results.

In another aspect, the interface is modified after being provided to theclient device 302. When the interface includes dynamic script thatrequests for updates to the results—to maintain updated results as thequery change—the query receiver 326 and query monitor 328 may providethe content corresponding to the new query and any descriptor from themodule 334 to modify the interface. As previously noted, modification ofthe interface includes modification of portions of the interface and/ormodification of the results and/or categories for the results. In afurther aspect, module 336 and/or module 338 modify the results and/orcategories as the interface renders on the client device 302. In such anaspect, a slot or allocated area is designed in the interface forproviding updated information or for providing most relevant newinformation to the client device 302. The slot or allocated area is adynamic area to incorporate the new information in a dynamic manner, andrepresents an updated look and feel for the interface. Moreover, the useof the interface provides a graphical user interface (GUI) that isdynamic—offering accurate and up-to-date content that is distinct fromstatic content of search results previously described. The use of suchinterfaces ensure that real estate in the GUI exists to present existingcontent to a submitted query as well as to modify the interface foraccurate and new information when the query is changed. Retrievedcontent from the content database 320 and from the index database 322 issubject to the formatting and presentation in the interface as describedby formatting rules of the interface generator 338.

Further, in an aspect to reduce processing time required to respond toqueries and to reduce latency is display or dynamic display of queryassists, a category-specific searching process may be used along withone or more embodiments of the disclosure. In this aspect, when thequery is received, the query receiver 326, along with the query monitor328, and query reviewer 330 (or one of these modules) may be configuredto determine that the query is associated with a category based on aclassification of at least one portion of the query in a classifieddataset that provides categories and content. A determination is madefor category-specific content for the category. With this information,at least one of the portions of the descriptors from thecategory-specific content may be identified to provide the query assist.In this manner, the system is able to respond faster using descriptorsfrom within the category alone instead of the entire index database ofdescriptors. Furthermore, portions of the feedback as the descriptorsmay be indexed with an association to the category-specific content.This may by the review indexer module 334. As a result, the portions ofthe descriptors for the combination with the query may be provided whenthe category-specific content is determined as being in the contentcategory for the query.

FIG. 4 illustrates an example 400 of a window of an interface 402 thatis modifiable and can be presented in accordance with variousembodiments. This example, like the example of FIG. 1 utilizes anelectronic marketplace as the content at issue. In the example 400, likein example 100 of FIG. 1, the window may be a web browser or astand-alone application for interacting with interface 402 of theelectronic marketplace. A user may search or interact with the interface402 via a search field 408 or via selecting various content 406-418. Aquery 418 is provided in the search field 408 in one implementation ofthe searching or the interaction with interface. When the submit optionis selected, the search may be initiated and processed on the computeror on a server as discussed in the implementations of FIGS. 1-3.Alternatively, the search field 408 is dynamic and responds to lettersentered as they are entered. In example 400, a query 418 for SHOES isentered into the search field 408, and results are provided in section404 of the interface 402. As illustrated, there are three results410-414 on display, with an indication for more results (e.g., downarrow with text “MORE” at the bottom of the page 402 or the grey scrollindicator on the right of the page 302) if the display content wasscrolled down.

Result 416, while illustrated in the search results may load after theexisting results—i.e., after results 410-414, are loaded. Categories406A, 406B, and 406D within the results are presented on one side of theresults, while sponsored content may be displayed on other availablearea 406C. Category 406D may be a new category based on machine learningfrom the reviews left following confirmed purchases of an item. Forexample, commonly used terms associated with reviews for searches andpurchases of SHOES are variations of HIKING and RUNNING. As a result,the machine learning using the previously described neural network(s)may then use the learning that the word SHOES bear semantic similarityto HIKING and RUNNING. Interface 402 may be modified by a query assistmodule of the system to include new categories 406D for HIKING andRUNNING—associated with the query SHOES 418.

Results 410-414 are illustrated as results that modify a prior set ofresults provided for a default listing for the interface; e.g.,interface 102 of FIG. 2. In particular, a first interface, e.g.,interface 102, may be provided with a listing of popular products at thetime of the query. Subsequently, responsive to a new query that may bedetermined as associated with a stored query, a query assist menu 420may be provided. Query assist menu 420 at least modifies the firstinterface. The query assist menu 420 may also be provided over thepopular products of the first interface. In the query assist menu 420,there is area 420A provided for one or more query assists—e.g., “Shoesfor Running ‘Sneakerun's sneaker is the lightest running . . . ’ 420B,“Shoes for Hiking” 420C, and “Shoes for Cold Weather Climbing” 420D. Thequery assist includes selectable links titled with the query anddescriptors or portions of descriptors, as explained with respect toFIGS. 2 and 3. In an alternate implementation, an area 416A may beprovided in interface 402 with the query assist for the query. Onselection of one of the selectable links, the interface is modified (oronly area 404 is modified) to display items of the listed items that areassociated to a descriptor underlying the selected selectable link.Furthermore, the modification to the interface may be to rearrange orrank the displayed items as most relevant to the selected selectablelink. One of ordinary skill would understand that example 400 providesresults 410-414 in interface 402 as an example only, but that if thequery is not submitted, then the query assist menu 420 may be providedover any prior listing of items or results to invite a selection fromthe user entering the query. When a selection 420E of a selectable linkfrom the query assist menu 420 is made, then the results 410-414 areprovided.

Result 410-414 includes shoes for running corresponding to selectedselectable link 420B, which is highlighted by a highlighting to indicateselection 420E. As such, the query is associated with the descriptorRUNNING from prior reviews for the items in results 410-414. Inparticular, however, at least one item, such as item 410, which is alsohighlighted, may have the closest review using words that aresemantically closest to the word RUNNING and is provided with prominencein the results. Separately, additional selectable links 416A may beindicated by special bordering 418B or markings, e.g., marking 418Astating NEW as to the query assists available as responsive to thequery. In an alternate implementation, a highlight or marker of any sortmay be applied to the query assist or to the top item portion of theresults in section 404 to reflect that dynamic modification hasoccurred. The example marked highlight 418A is illustrated over the slot418B to reflect this. In addition, a decay function may be associatedwith the marked highlight 418A or slot 418B. The decay function, in oneexample, may cause the highlight to draw the customer's attention to itand then fade over the decay time set in the decay function. Such animplementation further improves the user interface displayed in theexample electronic marketplace as discussed above. For example, thedecay function improves the ability of the computer to displayinformation and interact with the user through the use of contentmodification that dynamically indicates relevant information to theuser's query.

In aspects of this disclosure, other locations for the slot 418B may beprovided in the interface 402. Further, the title or the slot mayinclude embedded hyperlinks as selectable links to results pertaining tothe particular selectable link or to filter the existing results todisplay the results pertinent to the particular selectable link. Inaddition, excerpt 416B, from the feedback, may be provided with thequery assist in both sections 416A and in the query assist menu 420. Inanother aspect, along with or separate from the marked highlight 418A,the modified portion of the interface 402 might be subject to the decayfunction as well. For example, the modified portion—the slot 416A and/orthe query assist menu 420—may fade to the unmodified portion or todisplay results best matching the query. Alternatively, a hover-overevent may be implemented for a cursor action. Here, a timing functiondetermines the hover-over time of a cursor over the modified portion(e.g., marked highlight boundary of slot 416A). As such, when the cursorhovers over the query assist area 416A for the time specified in thetiming function, the modification may remove the modification tointerface 402.

In another example aspect, the excerpts from the feedback 416B may bedisplayed as an image from the review with information of the reviewerprovided that permission is granted to share the review. Such excerptsmay be retrieved as part of the indexing process described withreference to at least FIGS. 2, 3. In an example, the system of thedisclosure herein is available to modify any content than an interfacefor results. For example, in audio-based or image-based searchingprocess, a similar manner as described above is applicable. For areceived audio-based or image-based query, semantically similar or sameaudio or images from reviews provided by users for past purchases of arelated item are generated. The semantically similar or same audio orimages may be extracted and indexed along with identifiers for the queryand the item. Video searching is similarly possible using frames withinreview video demonstrating the product that is produced as part of areview. Portions of the video determined to include the product and anactivity may be indexed, for instance. In an example, using frames froma video, actions of a participating individual may be determined using astick model for joints of the participating individual and comparingmotion of the stick model with a trained neural network of featurestypical for certain activities like running, playing a sport, walking,etc. In an example, audio-based searching may use text-to-audioread-back of reviews, where the text reviews are read-back with amodification to an audio-based set of results that are being announcedin response to an audio-based query.

In an aspect, the present system supports or enables ranking ofdescriptors to select a descriptor associated with a confirmed purchasethat has a threshold number of purchases. To do so, the systemdetermines a first identifier for an individual one of the items. Thismay be by the review indexer. In an example, for various descriptors,such a ranking process is pre-computed and indexed with the descriptors,thereby providing associated descriptors a ranking to one or more partsof a query. Then the descriptors may be indexed along with the firstidentifier. A determination may also be made for second identifiers forindividual ones of the descriptors and the descriptors may be indexedalong with the second identifiers. In an example, the second identifiersmay be ranked first to select the portions of the descriptors is enabledsuch that a number of purchases that satisfies a threshold is returned.The number of purchases is a reference from after a selection of anassociated selectable link that comprises the portion of thedescriptors. In this manner, it is ensured that the ranking follows frompurchased items that are likely to be purchased once an associated queryassist is provided in response to a query.

Results may be, therefore, grouped by latent interest (activity,audience, etc) as per categories or the menu of selectable links. In anexample, the grouping is similar to a ranking of the most relevantresults in a first page or first entries of the listing, followed byremaining results. This may be seen as an alternative or in addition toother ranking methods. In an example, auto-completion in the searchquery is more relevant using query assists, so that when an query isprovided for “headphones,” then the search bar suggests “headphones . .. for airplane travel” or plainly, provides an selectable link of “forrunning” as an available result or for selection. In response to a queryfor “backpacks,” for instance, the search bar suggests “for parents” or“for hiking” based on these terms appearing in reviews left by customerswho previously purchased select items using the query in searches priorto the current search provided. The present method and systemsupplements other methods of query suggestion, for example based onfrequency of usage.

Recommendation of buying guides and reviews that address activities andaudiences may be another example use of the present disclosure, wherethe recommendations are related to the search query. For example, inresponse to the query “mens shoes,” an electronic marketplace maysuggest a buying guide that discusses different types of hiking shoesand what to consider when purchasing them, in addition to productsuggestions previously discussed. For a query of “alarm clock,” theelectronic marketplace may suggest alarm clocks for particularaudiences, such as deaf and hard of hearing, or heavy sleepers. In eachof the above examples, the results may additionally include annotationsusing excerpts and not just the descriptors.

FIG. 5 illustrates an example 500 of semantic processing for machinelearning in accordance with various embodiments that may be applied toachieve the above modification of the interface. The machine learning ofFIG. 5 may be implemented in server modules 324 to find descriptors in areview with matches to queries and to items identified as part ofconfirmed purchases. When the descriptors are found as identified by amachine learning algorithm, including following the process in FIG. 5,then the descriptors form a basis for providing query assist tosubsequent queries that are similar to the queries for which thedescriptors are found. In an example, the descriptors may be identifiedfor training a neural network to a query or for training a neuralnetwork to multiple queries, or for training multiple neural networks toquery or to multiple queries, all prior to applying the trained neuralnetwork or trained neural networks for determining descriptors forsubsequent queries. For the machine learning part, as illustrated inexample 500, the reviews or entries 506A-D for the confirmed purchasesare provided as individual words or phrases to the machine learningalgorithm. In example 500, each entry 506A-D represents a reviewidentified as associated to a query and to a confirmed purchase of anitem shows in response to the query. In an implementation, an individualword 502A/502B is taken and may be combined with each other word 502Dfrom a query to create a robust trained network for query recognition.Alternatively, the individual word 502A is taken and may be combinedwith the query to create a robust trained network.

Further, groups of words 502C/502E that are limited by a predefinednumber of words may be taken from a review and combined with othergroups of words from the query related to the review for training anetwork. Such training may be supervised or unsupervised. From theexample in FIG. 5, a machine learning trained dataset would be able todistinguish reviews as semantically similar to certain queries and tocertain items associated with those queries. For example, taking review506C, the words DRESS and SHOES may be taken alone and/or together withthe word FORMAL to form datasets for training a neural network. Theneural network is informed that the reviews 506A-D are from purchasesrelated to shoes, where the query is in the form of different referencesto shoes. As such, the neural network, once trained, may be a singularnetwork trained to distinguish future reviews for shoes by analysis ofterms provided in the review against the trained neural network.Alternatively, there may be singular neural network, one for each typeof shoe, based on the query, the review, and the item under confirmedpurchases. A subsequent query for shoes using any semantic language tothe language used to train the neural network will be classified by thetrained neural network as relating to one or more reviews. The one ormore reviews are to different types of shoes—e.g., shoes for running,hiking, etc. As such, a trained neural network would then suggest “shoesfor running” and “shoes for hiking” as the query for “shoes” isprovided.

In FIG. 5, a select root term and/or its grammatical variation may bechosen (e.g., reference numerals 502D) around which the training isperformed. For example, in the reviews 500, a similar or semanticallyclosest term to one or more query terms from the query is determined.Alternatively, one or more query terms of the query is used as a rootterm. Further, for a number of similar or same items confirmed aspurchased, the reviews may be grouped together with the queries used togenerate the similar or same items as a result. Then the one or morequery terms is determined from the grouped queries. This may be byselecting a most common term from the grouped queries or by selecting asignificant term from the grouped queries. Other statisticallyapplicable methods may be used—e.g., mean, median, or mode—to select asignificant term from the grouped queries. A similar process isseparately possible for the grouped reviews. Thereafter, the selectedterm is used as a root term.

Reviews are then parsed to include a respective root term 502D withother terms from each review to create a set that includes therespective root term. This is illustrated in extracted examples 508. Theextracted examples are used to train a neural network to determine arelationship between any of the review terms to the respective rootterm. Each term associated with the respective root term of theextracted samples 508 then provides context to the root term and helpswith the machine learning process. The remaining queries 504 in reviews500 may be rejected as not relevant, unless a neural network is built toprovide relevance to these terms. As a result, instances of words orphrases 502A-E are associated with groups or individual words fortraining a neural network to create a dataset of recognized words orphrases associated with a trained word or phrase (e.g., the root termsin example 500). In the machine learning process, extracted samples 508are extracted from the reviews 500 and include one root term (and/or itsgrammatical variation) 502D for example purposes.

The machine learning process trains a neural network (NN) to recognizethat SHOE AND SNEAKER or SHOE AND RUNNING as sharing a contextualrelationship such as a semantic relationship (e.g., used together in areview). Similarly, DRESS AND SHOES, and SHOE AND COLD WEATHER sharesemantic relationships for being used with each other. A trained neuralnetwork using the above extracted samples 508 may then respond to asubsequent query for SHOE entered to the trained neural network byidentifying LIGHTEST, RUNNING, TOUGH, or COLD WEATHER as query assistsand/or categories. This may be a determination process for descriptorsthat provide detail responsive to the query. So the query for SHOEgenerates one or more types of query assists that include thedescriptors—e.g., based on quality: SHOES THAT ARE LIGHTEST or SHOESTHAT ARE TOUGHEST; or based on activities: SHOES FOR RUNNING, SHOES FORHIKING, SHOES FOR COLD WEATHER, etc. There are large numbers of suchqueries that are utilized in the machine learning process and thatprovide similar contextual input. A robust dataset is enabled byproviding as many qualified review terms and associated query term—assets—for confirmed purchases to train the neural network.

Further, while the extracted samples 508 are illustrated as a root wordor phrase and associated word or phrases, a window of three or moreterms may be used to provide additional context. When the three or moreterms are taken together, they may form an excerpt that is also used toprovide detail responsive to the query. The neural network, in such animplementation, may be trained to recognize that each root word orphrase may be semantically related with two or more associated terms.While providing training vectors 510 for each word or phrase, it may beprudent to use a more robust training vector. The training vectors 510may represent a single word with 0s in all rows, but a 1 in a single rowto represent a word—e.g., SHOE or SHOES. This is also referred to as aone-dimensional representation of a word. The training vectors 510 maybe used as provided, but an option to use distributed training vectors600, as illustrated in FIG. 6A, is also available. The distributedtraining vectors may be a transformation of one-dimensional trainingvectors to form a multi-dimensional representation of words. As such,this transformation may be seen as a process of determining vector ofsemantic terms (e.g., a sentence) by averaging individual terms in theindividual vectors.

In an implementation, the one-dimensional representation of a word maybe prepared for the transformation to a multi-dimensional representationof words based on a matrix multiplication with a feature matrix. Thefeature matrix may provide the word's relationship to other wordsforming the multi-dimensional representation. For example, using asingle word's one-dimensional training vector, a neural network with ahidden layer and multiple related words may be trained to recognize thesingle word from multiple related words. In such an implementation, backpropagation of error and feed-forward processes may correct initialrandom values in the hidden layer to an accurate representation of amulti-dimensional vector for the word's relation to other words. In FIG.6A, the words in the query of FIGS. 1, 4, and 5 are provided incomparison with other words generally available in a database of termsthrough a multi-dimension vector.

In another example aspect, word-to-word comparison may be performed, andmay be extended to multiple words in a window of words. A trainingvector for a word, as illustrated in reference number 510, may be firstconverted into a feature representation using a feature matrix. Afeature matrix is composed of various values associated with uniquefeatures in text. For example, words with known relationships to otherwords may be a feature predefined in the feature matrix. In an example,the feature predefined in the feature matrix may be predefined languageassociated with at least one or more of activities, audiences, andinterests. The distributed representation vectors 600 may represent onesuch implementation. The training vector for a word may be multipliedwith the feature vector to provide the feature representation. As thetraining vector for each word is a single row of multiple 0s and asingle 1 entry, the resulting feature representation will be a singlerow of multiple columns of values. To find a relationship between words,two respective feature representations—one for each word—is multipliedand fed to a classifier. In an example the classifier may be a softmaxclassifier which is a normalized function that marginalizes the largestvalue from the lower values in a provided input. As a result, highlyrelated words are classified in a manner to distinguish a lesserrelationship word-pair.

For example, the multiplied values from the feature representations ofword A with a word B may provide a singular value. A similar evaluationof word A may be performed with multiple words. Following this, theclassifier may be applied to the collection of singular values toprovide a vector distribution of the words associated with word A indifferent levels of closeness (e.g., similarly, same word, or semanticsimilarity). The closeness demonstrates a relationship from prior use ofthe words associated with word A. The vector distribution may representa trained network which will be able to predict a word to output whenthe input word is Word A, following from the above example. Consideringthe above application in a multi-dimensional plane, with multiple wordsand relationships, a trained neural network will be able to identifyrelationships based on the numerical values associated with words. Assuch, an input of two or three words allows the trained neural networkto predict a word that is bears a relationship to both words. Such atrained neural network may also be designed to provide a level ofcloseness (semantic relationship or similarity) between words in areview to a query.

FIG. 6B illustrates another intermediate processing feature formodifying content based in part on a query. When a trained neuralnetwork indicates that semantic relationships exists between terms inthe review and terms of query to be stored for future queries, then thetrained neural network may be configured to store and provide suchinformation. In FIG. 6B, the information as to semantic relationships isprovided in the form of a graph 602. For new query words SHOE and SHIRT,the graph demonstrates what the trained neural network may see—includingrelationships of these words with respect to certain review terms—e.g.,RUNNING, SNEAKER, and HIKING, as demonstrated in the feature matrix 600of FIG. 6A. While the graph is provided to visualize distances betweenwords as defined in their determined semantic relationships, it isunderstood that a configured system may not graph the relationships, butmerely provide the outputs. As a result, the closest determined terms,by semantic relationships, may have the least distance as calculated bya cosine distance measure or a Euclidean distance measure—oncenormalized.

In an application of the above trained neural network, once acorrelation of descriptors to queries is completed by generation of thetrained neural network, new query terms may be input to the trainedneural network to get the associated descriptors that are not merely aword match to the query, but include a semantic match as well. In animplementation, the above machine learning is performed in a continuousmanner, but at least after reviews for recently purchased items isprovided with their associated queries. Such an implementationrepresents pre-commutation to improve results for future searches and tomake the computing process more efficient. The pre-computation may occurin anticipation of further queries to items in the content databasebased at least in part on the new reviews published in one or morewebsites. In an example, the pre-computation process monitors for thenew reviews after confirmed purchases and stores semantically similarterms to the queries, but also indexes reviews for subsequent queries.This improves latency of the query process from users' perspective, butalso enables efficient computing as it reduces the burden on the contentserver (including its related computing devices and modules) to generatesemantic descriptors for the query assist on-the-fly. The combination ofpre-computed modification at near real-time modification of an interfacewith dynamic modification may also be implemented depending on work-loadat the content server. At high traffic times, the work-load may behigher and the demand for resources may need to be balanced with respectto the in-demand content. In such instances, pre-computing may betriggered to benefit the dynamic modification for the interface.

As a result, the use of the present content searching processadditionally provides a solution to a network and computer related issueof latency and traffic management for high traffic networks. A user ableto secure their specific match, via the query assist, may not browsethrough multiple pages of search results or may not select to openmultiple pages of product information from the results. This reducestraffic to the content server (and related computing devices). This alsoreduces the work-load to these devices or allows the devices to be usedto perform other tasks—e.g., improving the dataset—than providing pagesof results. In addition, this also removes from any requirement to storeresults for anticipated access when the system provides thequery-specific search results in the very first page. This alleviatesstorage issues as the content server processes query and retrieves datafor numerous queries every second.

Another technical benefit realized in the use of the present disclosureis the ability to efficiently use display space in the user interface ofthe electronic marketplace or content display. The specific query assistor categories may be provided in specific areas of the interface asillustrated in FIG. 4, with subsequent results provided in the remainingarea previously allocated for the purpose. The user interface may alsobe dynamically modified in certain areas executing the appropriatedynamic script to indicate the specific search results separately fromthe general search results that are based on prior searches. Such areascan include sponsored search results (e.g., paid content), includingadvertisements as to accessories for the new product. In an example,when a request is determined as from a mobile device or using aconnection deemed as limiting (e.g., cost prohibitive or highloss/latency connections), it may be most efficient to provide thespecific query assist prior to any kind of content results. This mayrequire dynamic modification of the display content to display the queryassist followed by specific content in response to a selection of aselectable link of the query assist. This represents an exact match fora user's query in accordance with the latest reviews provided in atimely manner—e.g., a new product launched is immediately reviewed anddynamic categories or query assist is provided for subsequent searchesinstead of the typical search results of a previously highest sellingproduct based on previous user behavior.

FIG. 6C illustrates another example of using knowledge graphs to findvariations of descriptors in reviews used in the machine learningprocess for query assists in accordance with various embodiments. InFIG. 6C, an example knowledge graph is provided. The example knowledgegraph represents entities using multiple equivalent names, which may bevariations of descriptors, for instance. For example, a review mayinclude terms for a television, including its alternative terms commonlyused—e.g., “tv” or “television.” This may also include slags ortangential language for television, including telly, LCD, LED, Display,or combinations of terms, including LCD display, for instance.Variations of terms as determined to be closest in the customer reviewsthen required treatment of the customer reviews as to the same item andas related to the same query. The variations are treated as equivalentidentifications of a primary term, i.e., television in this example.This process allows better or more coverage to the reviews that may beconsidered in the classification process for indexing descriptors, andalso allows for more descriptors to be incorporated into the indexingand machine learning processes.

In a further aspect, the knowledge graph 610 of FIG. 6C may includenames or representative entities using multiple equivalent names. Forexample, entity XYZ can have alternative names “tv” or “television.” Allthe alternative names in the customer review may be treated asequivalent identifications of entity XYZ, which allows for bettercoverage of items from the entity for instance. An aggregate confidencemay be assigned to the relationship for each word to the entity. Theaggregate confidence may be, in an example, a value from 0 to 1. Theaggregate confidence may be further taken for all relationships of termsfor an entity. This may be by a unique identifier associated with anitem that tracks back to a manufacturer, for instance. In an example,this unique identifier may be a Universal Product Code (UPC), anEuropean Article Number (EAN), an International Standard Book Number(ISBN), or an Amazon Standard Identification Numbers (ASINs). The ASINassociation, for example, may provide overall matches of an entity inall reviews for the ASIN underlying an item.

The present disclosure also supports or enables a probabilistic modelbetween queries and entities. In an example, behavioral data from pastpurchase and interaction logs related to a unique identifier, such asASINs, responsive to queries may be used to generate an associationscore between Query text and the ASIN. This may be given by probabilitymeasure P(A_i|Q_j), where A_i is a specific ASIN and Q_j is a specificquery. Entities in a knowledge graph may be identified from ASIN inreviews by an association strength between the ASIN and the entity. Sucha probability measure may be represented as P(A_i|E_k), where A_i is anASIN and E_k is an entity from the Knowledge Graph. A joint neuralnetwork model may be trained or built to predict the entities associatedwith a query by creating P(E_k|Q_i) from the above probabilityrepresentations. This model can be implemented at a query level. In anexample, this model may be implemented by treating each query asdistinct, or using a Long Short-Term Memory (LSTM) sequence model, whichpredicts from a sequence of characters in the query.

FIG. 7A illustrates an example process flow 700 to index and presentsearch results using feedback, in accordance with various embodiments.Sub-process 702 parses reviews for a first item from sources of digitalcontent, such as content websites or an electronic marketplace. Thereviews are for the first item that is provided responsive to a priorquery in the electronic marketplace and that has been subsequentlypurchased. Sub-process 704 determines descriptors, such as interestdescriptors, from the reviews. As previously discussed, this may be byselecting terms or phrases or by finding plainly similar terms in thereviews and by rejecting other language deemed unimportant to classifythe query. In addition, as the reviews are provided after an associatedfirst item, responsive to the prior query, is purchased, the descriptorsbear association to the first item. In an example, the determination ofdescriptors allows training of a neural network for classification ofthe descriptors for the prior query within classification areas definedin at least two dimensions—the descriptors forming one dimension andterms in the prior query forming another dimension. In an example, thetrained neural network is capable of finding semantic descriptors tosubsequent query terms. Additionally, subsequent reviews are used withsubsequent query terms to improve robustness of the trained neuralnetwork by further training. This process is as described with respectto FIGS. 5, 6A, and 6B. Alternatively, the neural network may beinitially trained with a vocabulary provided to the neural network andusing the processes in FIGS. 5, 6A, and 6B.

Sub-process 706 stores the descriptors with an association to the itemand to at least one prior query used to identify the first item. Insub-process 708, a query is received in the electronic marketplace. Insub-process 710, portions of the descriptors are retrieved that may beresponsive to the query. This may be by the processing the query asinput to the trained neural network to find semantic descriptors to thequery. This is as described in the above descriptors with respect toFIGS. 2-6B. Sub-process 712 verifies that portions of the descriptorsare determined as responsive to the query. When such verificationdetermines that portions of the descriptors are responsive to the query,sub-process 714 is performed. Otherwise, sub-process 708 is cycled forfurther parts to the query. For example, a user may type additionalwords or letters or phrases to define the query if there are no queryassists provided. Sub-process 714 then determines that the querycorresponds to second items that include the first item. Sub-process 716provides an interface to display a listing of the second items. A personof ordinary skill would understand from the present disclosure thatselectable links may be embedded as underlay with titles provided forplain reading of what the selectable links represent. In an example, ahyperlink may be embedded by a title provided to the hyperlink.Nevertheless, the display of a selectable link titled with query andportions of the descriptor is plainly a display of the title and theselectable link is part of that display. Each entry of the listing ofthe second items includes a selectable link that is titled with thequery in combination with at least one of the portions of thedescriptors. Further, in sub-process 718, in response to a selection ofthe selectable links, a modification to the interface is performed todisplay at least the first item.

FIG. 7B illustrates an example process flow 750 to index and presentsearch results using feedback, in accordance with various embodiments.Sub-process 752 selects feedback for one or more items purchased from anelectronic marketplace. Sub-process 754 generates descriptors from thefeedback. In sub-process 756, a determination is made that portion ofthe descriptors provides detail responsive to a query for the one ormore items in the electronic marketplace. In an example, the query isreceived after prior queries result in the feedback for items purchasedfrom the electronic marketplace, where the query is associated withprior queries and where the descriptors is associated with the items.Sub-process 758 verifies if portion of the descriptors provide detailresponsive to the query. When no descriptors provide detail responsiveto the query, more feedback may be selected to cycle throughsub-processes 752-758. When the descriptors are determined as providingthe detail responsive to the query, sub-process 760 is performed. Insub-process 760, an interface is displayed include selectable linkstitled with the query in combination with the portions of thedescriptors. In sub-process 762, in response to a selection of one ofthe selectable links, a portion of the items are displayed. A selectionof one of the selectable links may effectively filter the items toprovide at least the item and/or the portion of the item. For example, aportion of related items to a portion of the descriptors underlying theone selectable link may be displayed.

FIG. 8 illustrates a logical arrangement of a set of general componentsof an example computing device 800 that can be used to implement aspectsof the various embodiments. In this example, the device includes aprocessor 802 for executing instructions that can be stored in a memorydevice or element 804. As would be apparent to one of ordinary skill inthe art, the device 800 can include many types of memory, data storage,or non-transitory computer-readable storage media, such as a first datastorage for program instructions for execution by the processor 802, aseparate storage for images or data, a removable memory for sharinginformation with other devices, etc. The device may include a positionelement 812 to provide positioning for updated results based ongeographic position of the device 800. The device 800 will include sometype of display element 806, such as a touch screen or liquid crystaldisplay (LCD), although devices such as portable media players mightconvey information via other means, such as through audio speakers. Asdiscussed, the device in many embodiments will include at least oneinput element 818 that is able to receive conventional input from auser. This conventional input can include, for example, a push button,touch pad, touch screen, wheel, joystick, keyboard, mouse, keypad, orany other such device or element whereby a user can input a command tothe device. In some embodiments, however, such a device might notinclude any buttons at all, and might be controlled only through acombination of visual and audio commands using imaging element 808 andaudio element 810, such that a user can control the device withouthaving to be in contact with the device. In some embodiments, thecomputing device 800 of FIG. 8 can include one or more network interfaceelements 808 for communicating over various networks, such as a Wi-Fi,Bluetooth, RF, wired, or wireless communication systems. The device inmany embodiments can communicate with a network, such as the Internetusing communication component 914, and may be able to communicate withother such devices using this same or a similar component. Components802-814 and 818 may be powered by power component 916 using internal or,in combination with, an external power supply.

Example environments discussed herein for implementing aspects inaccordance with various embodiments are primarily Web-based, as relateto Web services and cloud computing, but it should be appreciated that,although a Web-based environment is used for purposes of explanation,different environments may be used, as appropriate, to implement variousembodiments. Client devices used to interact with various embodimentscan include any appropriate device operable to send and receiverequests, messages, or information over an appropriate network andconvey information back to a user of the device. Examples of such clientdevices include personal computers, smart phones, handheld messagingdevices, laptop computers, set-top boxes, personal data assistants,electronic book readers, and the like. The network can include anyappropriate network, including an intranet, the Internet, a cellularnetwork, a local area network, or any other such network or combinationthereof. Components used for such a system can depend at least in partupon the type of network and/or environment selected. Protocols andcomponents for communicating via such a network are well known and willnot be discussed herein in detail. Communication over the network can beenabled by wired or wireless connections, and combinations thereof usingcommunication component 814.

It should be understood that there can be several application servers,layers, or other elements, processes, or components, which may bechained or otherwise configured, which can interact to perform tasks asdiscussed and suggested herein. As used herein the term “data store”refers to any device or combination of devices capable of storing,accessing, and retrieving data, which may include any combination andnumber of data servers, databases, data storage devices, and datastorage media, in any standard, distributed, or clustered environment.The application server can include any appropriate hardware and softwarefor integrating with the data store as needed to execute aspects of oneor more applications for the client device, handling a majority of thedata access and business logic for an application. The applicationserver provides access control services in cooperation with the datastore, and is able to generate content such as text, graphics, audio,and/or video to be transferred to the user, which may be served to theuser by the Web server in the form of HTML, XML, or another appropriatestructured language in this example. The handling of all requests andresponses, as well as the delivery of content between a client deviceand a resource, can be handled by the Web server. It should beunderstood that the Web and application servers are not required and aremerely example components, as structured code discussed herein can beexecuted on any appropriate device or host machine as discussedelsewhere herein.

A data store can include several separate data tables, databases, orother data storage mechanisms and media for storing data relating to aparticular aspect. The data store is operable, through logic associatedtherewith, to receive instructions from a server, and obtain, update, orotherwise process data in response thereto. In one example, a user mightsubmit a search request for a certain type of item. In this case, thedata store might access the user information to verify the identity ofthe user, and can access the catalog detail information to obtaininformation about items of that type. The information then can bereturned to the user, such as in a results listing on a Web page thatthe user is able to view via a browser on the user device. Informationfor a particular item of interest can be viewed in a dedicated page orwindow of the browser.

Each server will include an operating system that provides executableprogram instructions for the general administration and operation ofthat server, and will include a non-transitory computer-readable mediumstoring instructions that, when executed by a processor of the server,allow the server to perform its intended functions. Suitableimplementations for the operating system and functionality of theservers are known or commercially available, and are readily implementedby persons having ordinary skill in the art, particularly in light ofthe disclosure herein.

The environment in one embodiment is a distributed computing environmentutilizing several computer systems and components that areinterconnected via communication links, using one or more computernetworks or direct connections. However, it will be appreciated by thoseof ordinary skill in the art that such a system could operate equallywell in a system having fewer or a greater number of components than aredescribed. Thus, the depictions of various systems and services hereinshould be taken as being illustrative in nature, and not limiting to thescope of the disclosure.

Various aspects can be implemented as part of at least one service orWeb service, such as may be part of a service-oriented architecture.Services such as Web services can communicate using any appropriate typeof messaging, such as by using messages in extensible markup language(XML) format and exchanged using an appropriate protocol such as SOAP(derived from the “Simple Object Access Protocol”). Processes providedor executed by such services can be written in any appropriate language,such as the Web Services Description Language (WSDL). Using a languagesuch as WSDL allows for functionality such as the automated generationof client-side code in various SOAP frameworks.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially-available protocols, such as TCP/IP, FTP, UPnP,NFS, and CIFS. The network can be, for example, a local area network, awide-area network, a virtual private network, the Internet, an intranet,an extranet, a public switched telephone network, an infrared network, awireless network, and any combination thereof.

In embodiments utilizing a Web server, the Web server can run any of avariety of server or mid-tier applications, including HTTP servers, FTPservers, CGI servers, data servers, Java servers, and businessapplication servers. The server(s) also may be capable of executingprograms or scripts in response requests from user devices, such as byexecuting one or more Web applications that may be implemented as one ormore scripts or programs written in any programming language, such asJava®, C, C # or C++, or any scripting language, such as Perl, Python®,or Tool Command Language (TCL), as well as combinations thereof. Theserver(s) may also include database servers, including withoutlimitation those commercially available from Oracle®, Microsoft®,Sybase®, and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers, or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (CPU), at least one inputdevice (e.g., a mouse, keyboard, controller, touch screen, or keypad),and at least one output device (e.g., a display device, printer, orspeaker). Such a system may also include one or more storage devices,such as disk drives, optical storage devices, and solid-state storagedevices such as random access memory (“RAM”) or read-only memory(“ROM”), as well as removable media devices, memory cards, flash cards,etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices will also include a numberof software applications, modules, services, or other elements locatedwithin at least one working memory device, including an operating systemand application programs, such as a client application or Web browser.It should be appreciated that alternate embodiments may have numerousvariations from that described above. For example, customized hardwaremight also be used and/or particular elements might be implemented inhardware, software (including portable software, such as applets), orboth. Further, connection to other computing devices such as networkinput/output devices may be employed.

Storage media and other non-transitory computer readable media forcontaining code, or portions of code, can include any appropriate mediaknown or used in the art, including storage media and communicationmedia, such as but not limited to volatile and non-volatile, removableand non-removable media implemented in any method or technology forstorage of information such as computer readable instructions, datastructures, program modules, or other data, including RAM, ROM, EEPROM,flash memory or other memory technology, CD-ROM, digital versatile disk(DVD) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by the a system device. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the variousembodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

What is claimed is:
 1. A computer-implemented method, comprising:parsing, from sources of digital content, reviews for a first item;determining interest descriptors from the reviews; storing the interestdescriptors with an association to the first item and to at least oneprior query used to identify the first item; receiving a query in anelectronic marketplace; determining that portions of the interestdescriptors provide detail responsive to the query; determining that thequery corresponds to second items that comprise the first item;displaying an interface comprising a listing of the second items,individual ones of the listing comprising a selectable link that istitled with the query in combination with at least one of the portionsof the interest descriptors; and in response to a selection of theselectable links, modifying the interface to display at least the firstitem.
 2. The computer-implemented method of claim 1, further comprising:generating the interface to comprise a first area for results associatedwith the query and to comprise a second area; and dynamically modifyingthe interface to comprise, in the first area or the second area, theportion of the listing of the second items.
 3. The computer-implementedmethod of claim 1, further comprising: determining that the query isassociated with a category based on a classification of at least oneportion of the query in a classified dataset that provides categoriesand content; determining category-specific content for the category; andidentifying the at least one of the portions of the interest descriptorsfrom the category-specific content.
 4. The computer-implemented methodof claim 3, further comprising: providing the interface to render on aclient device; receiving a second query in the interface; and based atleast in part on a script rendered with the interface, modifying theinterface to comprise third items that are responsive to the secondquery, individual ones of the third items comprising a second selectablelink that is titled with the second query in combination with at leastone second portion of the interest descriptors.
 5. Thecomputer-implemented method of claim 1, further comprising: parsing atleast one identifier of a content website from the content websites todetermine domain information; determining a confidence score associatedwith an entity associated with the domain based at least in part on thedomain providing information for the first item; and when the confidencescore exceeds a threshold value, providing the reviews from the contentwebsite for the interest descriptors.
 6. The computer-implemented methodof claim 5, further comprising: parsing the content websites to selectsets of contiguous words associated with the first item; when at leastone set of contiguous words is selected, incrementing a count; andweighing the confidence score favorably for the domain when the countsatisfies a threshold.
 7. The computer-implemented method of claim 1,further comprising: determining a purchase made in the electronicmarketplace for the first item following a search using the query;determining the reviews provided to the electronic marketplace andassociated with the purchase; parsing the reviews to select words orphrases that share a correlation or a semantic similarity to predefinedlanguage associated with at least one or more of activities, audiences,and interests; and classifying the select words or phrases as theinterest descriptors.
 8. A system, comprising: at least one processor;and a memory device including instructions that, when executed by the atleast one processor, cause the system to: select feedback for itemsobtained for consumption; generate descriptors from the feedback;receiving a query for the one or more of the items; determine thatportions of the descriptors provide detail responsive to the query;provide an interface to display selectable links titled with the queryin combination with the portions of the descriptors; and provide aportion of the items for display according to a selection of one of theselectable links.
 9. The system of claim 8, wherein the instructions,when executed by the at least one processor, further cause the systemto: for an individual letter, word, or phrase entered as part of thequery in a search field of an electronic marketplace, generate anindividual selectable link titled with a combination the individualletter, word, or phrase and the portions of the descriptors; dynamicallydisplay, in a first area of the interface, the individual selectablelink after entry of the individual letter, word, or phrase as part ofthe query in a search field of the electronic marketplace.
 10. Thesystem of claim 9, wherein the instructions, when executed by the atleast one processor, further cause the system to: dynamically display amenu in the first area of the interface for comprising the individualselectable link as part of the selectable links, the first area adjacentor contiguous to the search field of the electronic marketplace.
 11. Thesystem of claim 10, wherein the instructions, when executed by the atleast one processor, further cause the system to: receive a selection ofanother one of the selectable links; and modify a second area of theinterface to include select items of the portion of the items that areassociated with the individual selectable link.
 12. The system of claim11, wherein the instructions, when executed by the at least oneprocessor, further cause the system to: list the portion of the items inthe first area of the interface in a ranking to provide visibility of atleast one item that is associated with an item providing the portions ofthe descriptors that is used in a title for the individual selectablelink.
 13. The system of claim 8, wherein the instructions, when executedby the at least one processor, further cause the system to: determinethat the query is associated with a category based on a classificationof at least one portion of the query in a classified dataset thatprovides categories and content; determine category-specific content forthe category; and identify at least one of the portions of thedescriptors from the category-specific content.
 14. The system of claim13, wherein instructions, when executed by the at least one processor,further cause the system to: index portions of the feedback as thedescriptors along with an association to the category-specific content;and provide the portions of the descriptors for the combination with thequery when the category-specific content is determined as being in thecontent category for the query.
 15. The system of claim 8, whereininstructions, when executed by the at least one processor, further causethe system to: reject, from the feedback, articles and non-descriptors;classify remaining portions of the feedback, after the rejection, as thedescriptors; and index the descriptors for use with the query.
 16. Thesystem of claim 15, wherein instructions, when executed by the at leastone processor, further cause the system to: determine a first identifierfor an individual one of the items; and index the descriptors along withthe first identifier.
 17. The system of claim 15, wherein instructions,when executed by the at least one processor, further cause the systemto: determine second identifiers for individual ones of the descriptors;index the descriptors along with the second identifiers; and enableranking of the second identifiers to select the portions of thedescriptors that return a number of purchases that satisfies athreshold, the number of purchases after a selection of an associatedselectable link that comprises the portion of the descriptors.
 18. Anon-transitory computer-readable storage medium including instructionsthat, when executed by at least one processor of a computing system,cause the computing system to: select feedback for items obtained forconsumption; generate descriptors from the feedback; receive a query forthe one or more of the items; determine that portions of the descriptorsprovide detail responsive to the query; provide an interface to displayselectable links titled with the query in combination with the portionsof the descriptors; and provide a portion of the items for displayaccording to a selection from the selectable links.
 19. Thenon-transitory computer readable storage medium of claim 18, wherein theinstructions, when executed by the at least one processor, further causethe computing system to: for an individual letter, word, or phraseentered as part of the query in a search field of an electronicmarketplace, generate an individual selectable link titled with acombination the individual letter, word, or phrase and the portions ofthe descriptors; dynamically display, in an area of the interface, theindividual selectable link after entry of the individual letter, word,or phrase as part of the query in a search field of the electronicmarketplace.
 20. The non-transitory computer readable storage medium ofclaim 19, wherein the instructions, when executed by the at least oneprocessor, further cause the computing system to: dynamically display amenu in the area of the interface for comprising the individualselectable link as part of the selectable links, the area adjacent orcontiguous to the search field of the electronic marketplace.